{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 拉勾网Python职位数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import mpl\n",
    "\n",
    "mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体\n",
    "mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./lagou_python.txt\", sep=\"\\t\", encoding=\"gbk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
       "      <th>companyFullName</th>\n",
       "      <th>companyShortName</th>\n",
       "      <th>industryField</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>companySize</th>\n",
       "      <th>salary</th>\n",
       "      <th>workYear</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5920752</td>\n",
       "      <td>python开发工程师</td>\n",
       "      <td>62</td>\n",
       "      <td>北京字节跳动科技有限公司</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>C轮</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>20k-35k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6625369</td>\n",
       "      <td>Python开发工程师</td>\n",
       "      <td>85693960</td>\n",
       "      <td>北京艨艟信息技术有限公司</td>\n",
       "      <td>Games Vessel</td>\n",
       "      <td>游戏,软件开发</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7594821</td>\n",
       "      <td>python助教</td>\n",
       "      <td>147</td>\n",
       "      <td>北京拉勾网络技术有限公司</td>\n",
       "      <td>拉勾网</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>10k-15k</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   positionId positionName  companyId companyFullName companyShortName  \\\n",
       "0     5920752  python开发工程师         62    北京字节跳动科技有限公司             字节跳动   \n",
       "1     6625369  Python开发工程师   85693960    北京艨艟信息技术有限公司     Games Vessel   \n",
       "2     7594821     python助教        147    北京拉勾网络技术有限公司              拉勾网   \n",
       "\n",
       "  industryField financeStage companySize   salary workYear education  \n",
       "0         文娱丨内容           C轮     2000人以上  20k-35k     1-3年        本科  \n",
       "1       游戏,软件开发        不需要融资     50-150人  15k-30k     1-3年        本科  \n",
       "2          企业服务        D轮及以上   500-2000人  10k-15k     1-3年        本科  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 融资阶段的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19211a0d370>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"financeStage\").size().sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 公司规模的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x192118039d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"companySize\").size().sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 薪资分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 区间的低薪，每5K进行分区间\n",
    "df[\"low_salary\"] = df[\"salary\"].map(lambda x : int(x.split(\"-\")[0].replace(\"k\", \"\")))\n",
    "df[\"low_salary\"] = pd.cut(df[\"low_salary\"],[5, 10, 15, 20, 25, 30, 35, 40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19214c96ee0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"low_salary\").size().sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 融资阶段与薪资的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x192151178e0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n",
    "fig, ax = pyplot.subplots(figsize=(10, 6))\n",
    "sns.countplot(x=\"financeStage\", hue=\"low_salary\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
